TransferLab Training: Practical Anomaly Detection - Module 1: Introduction to Anomaly Detection

In this video, we'll dive into anomaly detection and its real-world applications. Together, we will explore the challenges and the different types of anomalies. Next, we'll discuss the contamination framework and conclude by introducing evaluation metrics tailored for anomaly detection, addressing the class imbalance problem along the way.
Accessing the resources of this course:
🔗 GitHub Repository: complete code and examples on our GitHub github.com/aai...
🌐 Website: digests of the latest research on the TransferLab’s website: transferlab.ai/
🎓 Full Course & Certification (For FREE): Enroll in our full video course available on our learning platform.
Complete the course at your own pace and earn a certificate for free to enhance your portfolio: lms.appliedai-...
00:10 What is an Anomaly?
02:55 Practical Relevance of Anomaly Detection (A.D.)
06:05 Relevance of Unsupervised Machine Learning in A.D.
07:32 The Contamination Framework
13:03 Evaluation Metrics for A.D. Systems
18:02 A.D. Using Distance Metrics
21:27 Exercise: Using Distance Metrics for A.D.
22:21 Solution: Using Distance Metrics for A.D.
30:01 Taxonomy of A.D. Approaches
The appliedAI Institute for Europe gGmbH is supported by the KI-Stiftung Heilbronn gGmbH.